code: def main(
      epochs: Param("Number of epochs", int) = 5,
      fp16: Param("Use mixed precision training", store_true) = False,
      dump: Param("Print model; don't train", int) = 0,
      runs: Param("Number of times to repeat training", int) = 1,
  ):
      "Training of Tabular data 'ADULT_SAMPLE'."
      path = rank0_first(untar_data, URLs.ADULT_SAMPLE)
      dls = get_dls(path)
      pr(f"epochs: {epochs};")
  
      for run in range(runs):
          pr(f"Run: {run}")
          learn = tabular_learner(dls, metrics=accuracy)
          if dump:
              pr(learn.model)
              exit()
          if fp16:
              learn = learn.to_fp16()
          n_gpu = torch.cuda.device_count()
          ctx = (
              learn.distrib_ctx
              if num_distrib() and n_gpu
              else learn.parallel_ctx
          )
          with ctx():
              learn.fit_one_cycle(epochs)
  
file_path: fastai/nbs/examples/train_tabular.py
imports: from fastai.basics import *
  from fastai.tabular.all import *
  from fastai.callback.all import *
  from fastai.distributed import *
  from fastprogress import fastprogress
  from fastai.callback.mixup import *
  from fastcore.script import *
label_desc: Use image node_id 3 for calling a global function with example usage: main(epochs, fp16, dump, runs) without return types